WHERE CAN WE GROW? MACHINE LEARNING TO PREDICT CLIMATE IMPACTS ON U.S. CORN AND SOYBEAN SUITABILITY

The main goal ofthis project is to apply machine learningmethods to the analysis of "Big" agricultural datatoextractcrop-specific suitabilityinformation relevant to sustainable agricultural intensification and climate change adaptation. This project will address three main research objectives:(1) develop a machine learning model that can estimate within-field crop suitabilitybasedon yield monitordatafor U.S. corn and soybean byconsidering site-specificclimate, soil, and terrain conditions, (2) expandthe machine learning modeltopredict crop suitability for the entireU.S.given climate, soil, and terrain conditions, and (3)usethisnational-scale suitabilitymodel to predict crop suitabilityresponse to climate change usingrepresentative concentration pathwaysdescribedin theCoupled Model Intercomparison Project.The specifics of eachobjectivearediscussed below.Objective 1.Develop machine learning models that can estimate within-field crop suitability on yield monitor farms for corn and soybean based on climate, soil, and terrain conditions.Sub-objective 1.1:Cleaning and preprocessing ofyield monitor dataset from over 60 U.S. farmsspanning 3-10 years per farm,collected as part of the DIFM researchproject.Sub-objective 1.2:Cleaning and preprocessing of soil, climate, and landscape terrain factors will beused as predictive variables in the models.Sub-objective 1.3:Model selection and feature selection.Three well-establishedMLmodels will be evaluated and compared for final use in this study: Random Forest, Support Vector Machine,andArtificial Neural Network. Model selection will be conducted based on interpretability, accuracy, and data and computational requirements. Iffeasiblebased on the amount and quality of data, preference will be given toadeep learning neural network model.Sub-objective 1.4:Model training and cross-fold validation. Bothclassification and regression approaches will be explored.Sub-objective 1.5:Model evaluation including quantitative error and accuracy metrics.Objective 2:Expand the machine learning model to predict crop suitability for the entire United States given climate, soil, and terrain conditions.Sub-objective 2.1:Cleaning and processing of national scale soil, climate, and terrain data.Sub-objective 2.2:Iterative scaling of model from objective 1 to be operational at the national scale.Sub-objective 2.3: Model training and predictive mapping of U.S. crop suitability.Sub-objective 2.4:Model evaluation and accuracy assessment using DIFM yield monitor data andcounty-level yields from the National Agricultural Statistics Service.Sub-objective 2.5:Sharevalidated suitability mapswith producers and house on a web-based tool hosted by USDA or using Google Earth Engine Apps.Objective 3:Use thenational-scalesuitabilitymodel to predict crop suitability response to climate change using representative concentration pathways described in the Coupled Model Intercomparison Project.Sub-objective 3.1:Clean and process climate data fromCoupled Model Intercomparison ProjectPhase5(CMIP5)climate projectionsfor input in to machine learning model.Sub-objective 3.2:Evaluation ofimpact of each RCP scenario on crop suitability. Special attention will be given to changing spatial patterns of crop suitabilitytoinform agricultural planning.Sub-objective 3.3:Development of maps and evaluationof areas ofhigh riskand opportunities forclimateadaptation.Sub-objective 3.4:Development of web-based app using Google Earth Engine that allows dynamic updating of suitability predictions based on new climate data.Data dissemination objectivesDevelopment of a web app in Google Earth Engine in consultation with extension professionalsDrafting of 2-3 manuscripts for publication in peer reviewed journals